Optimal experiment design in a filtering context with application to sampled network data

We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, we relate our work to the general problem of steady state optimal design for state space models.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS283 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks

Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.Comment: 6 page

Phase Space dynamics of triaxial collapse: Joint density-velocity evolution

We investigate the dynamics of triaxial collapse in terms of eigenvalues of the deformation tensor, the velocity derivative tensor and the gravity Hessian. Using the Bond-Myers model of ellipsoidal collapse, we derive a new set of equations for the nine eigenvalues and examine their dynamics in phase space. The main advantage of this form is that it eliminates the complicated elliptic integrals that appear in the axes evolution equations and is more natural way to understand the interplay between the perturbations. This paper focuses on the density-velocity dynamics. The Zeldovich approximation implies that the three tensors are proportional; the proportionality constant is set by demanding `no decaying modes'. We extend this condition into the non-linear regime and find that the eigenvalues of the gravity Hessian and the velocity derivative tensor are related as ${\tilde q}_d + {\tilde q}_v=1$, where the triaxiality parameter ${\tilde q} = (\lambda_{\mathrm{max}} - \lambda_{\mathrm{inter}})/(\lambda_{\mathrm{max}} - \lambda_{\mathrm{min}})$. This is a {\it new universal relation} holding true over all redshifts and a range of mass scales to within a few percent accuracy. The mean density-velocity divergence relation at late times is close to linear, indicating that the dynamics is dictated by collapse along the largest eigendirection. This relation has a scatter, which we show, is intimately connected to the velocity shear. Finally, as an application, we compute the PDFs of the two variables and compare with other forms in the literature.Comment: 23 pages (16 text+appendix); 11 figures, revised version accepted for publication in MNRA